"""可视化脚本里不依赖模型的部分。"""

import sys
from pathlib import Path

import pytest

pytest.importorskip("yaml")
import yaml  # noqa: E402

sys.path.insert(0, "scripts")
import visualize_detections as vd  # noqa: E402

from engine.detection import Box, from_yolo, match_boxes  # noqa: E402


def _match(predictions, truth):
    return match_boxes(predictions, truth)


@pytest.mark.parametrize(
    "truth,predictions,expected",
    [
        ([], [], "none"),                                                    # 负样本判对
        ([from_yolo(0, .5, .5, .2, .2)], [], "missed"),                      # 完全没检出
        ([], [Box(0, .4, .4, .6, .6, .9)], "extra"),                         # 背景上凭空框出
        ([from_yolo(0, .5, .5, .2, .2)], [Box(0, .41, .41, .59, .59, .9)], "ok"),
    ],
)
def test_outcome_categories(truth, predictions, expected):
    assert vd.outcome(len(truth), len(predictions), _match(predictions, truth)) == expected


def test_missed_takes_priority_over_extra():
    """一张图同时有漏检和误检时归为 missed——漏检更该先看。"""
    truth = [from_yolo(0, .2, .2, .1, .1)]
    predictions = [Box(0, .7, .7, .9, .9, .9)]
    assert vd.outcome(1, 1, _match(predictions, truth)) == "missed"


def test_label_path_swaps_images_for_labels():
    image = Path("/data/film/images/val/blackdot_0001.png")
    assert vd.label_path_for(image) == Path("/data/film/labels/val/blackdot_0001.txt")


def test_label_path_uses_the_last_images_segment():
    """路径里多处出现 images 时，只换最靠后的那一段。"""
    image = Path("/srv/images/film/images/train/a.png")
    assert vd.label_path_for(image) == Path("/srv/images/film/labels/train/a.txt")


@pytest.fixture
def dataset(tmp_path):
    for split in ("train", "val"):
        (tmp_path / "images" / split).mkdir(parents=True)
        (tmp_path / "images" / split / f"{split}_0001.png").write_bytes(b"")
    data_yaml = tmp_path / "data.yaml"
    data_yaml.write_text(
        yaml.safe_dump({
            "path": str(tmp_path), "train": "images/train", "val": "images/val",
            "nc": 2, "names": {0: "blackdot", 1: "grease"},
        }),
        encoding="utf-8",
    )
    return data_yaml


def test_load_dataset(dataset):
    classes, splits = vd.load_dataset(dataset)
    assert classes == ["blackdot", "grease"]
    assert sorted(splits) == ["train", "val"]
    assert splits["val"][0].name == "val_0001.png"


def test_load_dataset_skips_a_split_pointing_at_the_same_directory(dataset):
    """未划分的数据集 train 与 val 指向同一目录，不该把同一批图跑两遍。"""
    meta = yaml.safe_load(dataset.read_text(encoding="utf-8"))
    meta["val"] = meta["train"]
    dataset.write_text(yaml.safe_dump(meta), encoding="utf-8")
    _, splits = vd.load_dataset(dataset)
    assert list(splits) == ["train"]


def test_load_dataset_reports_a_missing_directory(dataset):
    meta = yaml.safe_load(dataset.read_text(encoding="utf-8"))
    meta["val"] = "images/nope"
    dataset.write_text(yaml.safe_dump(meta), encoding="utf-8")
    with pytest.raises(FileNotFoundError, match="不存在"):
        vd.load_dataset(dataset)


# ---------------- 产出的目录结构 ----------------


def test_class_dir_uses_the_dominant_ground_truth_class():
    classes = ["blackdot", "grease", "normal"]
    truth = [from_yolo(1, .5, .5, .2, .2), from_yolo(0, .1, .1, .1, .1), from_yolo(1, .8, .8, .1, .1)]
    assert vd.class_dir_for(truth, classes, "grease_0007") == "grease"


def test_class_dir_falls_back_to_the_filename_prefix():
    """负样本没有真值框，按 <类别>_<序号> 的命名归类。"""
    classes = ["blackdot", "grease", "normal"]
    assert vd.class_dir_for([], classes, "normal_1234") == "normal"


def test_class_dir_falls_back_to_background_for_unknown_names():
    classes = ["blackdot", "grease", "normal"]
    assert vd.class_dir_for([], classes, "IMG_20260101") == "background"
    assert vd.class_dir_for([], classes, "nofix") == "background"